Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Integrating Reinforcement Learning with Multi-Agent Techniques for Adaptive Service Composition

Integrating Reinforcement Learning with Multi-Agent Techniques for Adaptive Service Composition Integrating Reinforcement Learning with Multi-Agent Techniques for Adaptive Service Composition HONGBIGN WANG, XIN CHEN, and QIN WU, Southeast University QI YU, Rochester Institute of Technology XINGGUO HU, Southeast University ZIBIN ZHENG, The Chinese University of Hong Kong ATHMAN BOUGUETTAYA, The University of Sydney Service-oriented architecture is a widely used software engineering paradigm to cope with complexity and dynamics in enterprise applications. Service composition, which provides a cost-effective way to implement software systems, has attracted significant attention from both industry and research communities. As online services may keep evolving over time and thus lead to a highly dynamic environment, service composition must be self-adaptive to tackle uninformed behavior during the evolution of services. In addition, service composition should also maintain high efficiency for large-scale services, which are common for enterprise applications. This article presents a new model for large-scale adaptive service composition based on multiagent reinforcement learning. The model integrates reinforcement learning and game theory, where the former is to achieve adaptation in a highly dynamic environment and the latter is to enable agents to work for a common task (i.e., composition). In particular, we propose a multi-agent Q-learning algorithm for service composition, which is expected to achieve better http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Autonomous and Adaptive Systems (TAAS) Association for Computing Machinery

Integrating Reinforcement Learning with Multi-Agent Techniques for Adaptive Service Composition

Loading next page...
 
/lp/association-for-computing-machinery/integrating-reinforcement-learning-with-multi-agent-techniques-for-FtI24g0xke
Publisher
Association for Computing Machinery
Copyright
Copyright © 2017 by ACM Inc.
ISSN
1556-4665
DOI
10.1145/3058592
Publisher site
See Article on Publisher Site

Abstract

Integrating Reinforcement Learning with Multi-Agent Techniques for Adaptive Service Composition HONGBIGN WANG, XIN CHEN, and QIN WU, Southeast University QI YU, Rochester Institute of Technology XINGGUO HU, Southeast University ZIBIN ZHENG, The Chinese University of Hong Kong ATHMAN BOUGUETTAYA, The University of Sydney Service-oriented architecture is a widely used software engineering paradigm to cope with complexity and dynamics in enterprise applications. Service composition, which provides a cost-effective way to implement software systems, has attracted significant attention from both industry and research communities. As online services may keep evolving over time and thus lead to a highly dynamic environment, service composition must be self-adaptive to tackle uninformed behavior during the evolution of services. In addition, service composition should also maintain high efficiency for large-scale services, which are common for enterprise applications. This article presents a new model for large-scale adaptive service composition based on multiagent reinforcement learning. The model integrates reinforcement learning and game theory, where the former is to achieve adaptation in a highly dynamic environment and the latter is to enable agents to work for a common task (i.e., composition). In particular, we propose a multi-agent Q-learning algorithm for service composition, which is expected to achieve better

Journal

ACM Transactions on Autonomous and Adaptive Systems (TAAS)Association for Computing Machinery

Published: May 29, 2017

There are no references for this article.